U.S. patent application number 15/947156 was filed with the patent office on 2019-10-10 for cognitive content mapping and collating.
The applicant listed for this patent is International Business Machines Corporation. Invention is credited to Danish Contractor, Ying Li, Mukesh Mohania, Prasanna C. Nair, Bikram Sengupta.
Application Number | 20190311639 15/947156 |
Document ID | / |
Family ID | 68097331 |
Filed Date | 2019-10-10 |
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United States Patent
Application |
20190311639 |
Kind Code |
A1 |
Contractor; Danish ; et
al. |
October 10, 2019 |
Cognitive Content Mapping and Collating
Abstract
Methods, systems, and computer program products for cognitive
content mapping and collating are provided herein. A
computer-implemented method includes identifying resources relevant
to an existing course; partitioning, based on pre-determined
partitioning parameters, (i) the existing course into multiple
portions and (ii) the resources into multiple portions; detecting
content coverage gaps in the existing course by semantically
comparing (i) the multiple portions of the existing course with
(ii) the multiple portions of the resources; retrieving, based on
the detected content coverage gaps, at least one of the multiple
portions of the resources; and generating an updated version of the
existing course by incorporating the at least one retrieved portion
of the resources into the existing course.
Inventors: |
Contractor; Danish; (New
Delhi, IN) ; Li; Ying; (Mohegan Lake, NY) ;
Mohania; Mukesh; (Forrest, AU) ; Nair; Prasanna
C.; (Bangalore, IN) ; Sengupta; Bikram;
(Bangalore, IN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
International Business Machines Corporation |
Armonk |
NY |
US |
|
|
Family ID: |
68097331 |
Appl. No.: |
15/947156 |
Filed: |
April 6, 2018 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 10/06395 20130101;
G09B 5/06 20130101; G09B 5/00 20130101; G09B 7/00 20130101; G06Q
10/105 20130101 |
International
Class: |
G09B 5/06 20060101
G09B005/06; G06Q 10/06 20060101 G06Q010/06; G06Q 10/10 20060101
G06Q010/10 |
Claims
1. A computer-implemented method comprising steps of: identifying
one or more resources relevant to an existing course; partitioning,
based on one or more pre-determined partitioning parameters, (i)
the existing course into multiple portions and (ii) the one or more
resources into multiple portions; detecting one or more content
coverage gaps in the existing course by semantically comparing (i)
the multiple portions of the existing course with (ii) the multiple
portions of the one or more resources; retrieving, based on the one
or more detected content coverage gaps, at least one of the
multiple portions of the one or more resources; and generating an
updated version of the existing course by incorporating the at
least one retrieved portion of the one or more resources into the
existing course; wherein the steps are carried out by at least one
computing device.
2. The computer-implemented method of claim 1, wherein said
semantically comparing comprises comparing (i) metadata associated
with the multiple portions of the existing course with (ii)
metadata associated with the multiple portions of the one or more
resources.
3. The computer-implemented method of claim 1, wherein the existing
course comprises an instructor-led training course.
4. The computer-implemented method of claim 1, wherein the one or
more resources comprise one or more electronic learning
courses.
5. The computer-implemented method of claim 1, wherein said
partitioning comprises semantically partitioning (i) the existing
course into multiple consumable portions and (ii) the one or more
resources into multiple consumable portions.
6. The computer-implemented method of claim 1, wherein the one or
more pre-determined partitioning parameters comprises one or more
learning objectives.
7. The computer-implemented method of claim 1, wherein the one or
more pre-determined partitioning parameters comprises one or more
keywords.
8. The computer-implemented method of claim 1, wherein said
detecting the one or more content coverage gaps in the existing
course comprises implementing one or more multimedia analytics
techniques.
9. The computer-implemented method of claim 1, wherein said
detecting the one or more content coverage gaps in the existing
course comprises mapping (i) the multiple portions of the existing
course to (ii) the multiple portions of the one or more
resources.
10. The computer-implemented method of claim 1, comprising:
determining affinity of the at least one retrieved portion of the
one or more resources with the existing course.
11. The computer-implemented method of claim 1, comprising:
standardizing formatting of (i) the existing course and (ii) the
one or more resources.
12. The computer-implemented method of claim 1, comprising:
generating, within the updated version of the existing course, one
or more captions in connection with one or more items of multimedia
content.
13. The computer-implemented method of claim 1, comprising:
generating, within the updated version of the existing course, one
or more items of bridge text linking two or more of multiple
portions of the updated version of the existing course.
14. The computer-implemented method of claim 1, comprising:
exporting the updated version of the existing course to one or more
formats.
15. The computer-implemented method of claim 14, wherein the one or
more formats comprise at least one of (i) a standard courseware
format, (ii) a sharable content object reference model, (iii) a
custom format, and (iv) a collection of documents in a zip
file.
16. A computer program product comprising a computer readable
storage medium having program instructions embodied therewith, the
program instructions executable by a computing device to cause the
computing device to: identify one or more resources relevant to an
existing course; partition, based on one or more pre-determined
partitioning parameters, (i) the existing course into multiple
portions and (ii) the one or more resources into multiple portions;
detect one or more content coverage gaps in the existing course by
semantically comparing (i) the multiple portions of the existing
course with (ii) the multiple portions of the one or more
resources; retrieve, based on the one or more detected content
coverage gaps, at least one of the multiple portions of the one or
more resources; and generate an updated version of the existing
course by incorporating the at least one retrieved portion of the
one or more resources into the existing course.
17. The computer program product of claim 16, wherein said
detecting the one or more content coverage gaps in the existing
course comprises mapping (i) the multiple portions of the existing
course to (ii) the multiple portions of the one or more
resources.
18. A system comprising: a memory; and at least one processor
operably coupled to the memory and configured for: identifying one
or more resources relevant to an existing course; partitioning,
based on one or more pre-determined partitioning parameters, (i)
the existing course into multiple portions and (ii) the one or more
resources into multiple portions; detecting one or more content
coverage gaps in the existing course by semantically comparing (i)
the multiple portions of the existing course with (ii) the multiple
portions of the one or more resources; retrieving, based on the one
or more detected content coverage gaps, at least one of the
multiple portions of the one or more resources; and generating an
updated version of the existing course by incorporating the at
least one retrieved portion of the one or more resources into the
existing course.
19. A computer-implemented method comprising steps of: identifying
one or more external resources relevant to an existing training
course associated with a given enterprise; partitioning, based on
one or more pre-determined partitioning parameters, (i) the
existing training course into multiple consumable modules and (ii)
the one or more external resources into multiple consumable
modules; detecting one or more content coverage gaps in the
existing training course by semantically comparing (i) the multiple
consumable modules of the existing training course with (ii) the
multiple consumable modules of the one or more external resources;
automatically creating one or more work items, wherein the one or
more work items provide (i) instructions for generating an updated
version of the existing training course based on incorporating at
least one of the multiple consumable modules of the one or more
external resources into the existing training course and (ii) an
estimated effort associated with generating the updated version of
the existing training course; and outputting the one or more
created work items to one or more instructional designers
associated with the given enterprise; wherein the steps are carried
out by at least one computing device.
20. The computer-implemented method of claim 19, wherein said
semantically comparing comprises comparing (i) metadata associated
with the multiple portions of the existing course with (ii)
metadata associated with the multiple portions of the one or more
resources.
Description
FIELD
[0001] The present application generally relates to information
technology, and, more particularly, to instructional information
management.
BACKGROUND
[0002] Enterprise and workplace training has traditionally involved
face-to-face sessions with instructors, using training materials
that are generally text-based and related to one or more learning
objectives of the organization. Increasingly, enterprises are
attempting to phase-out such manual text-based approaches with
electronic learning (e-learning) programs. However, instructional
design teams in such enterprises generally have to manually review
e-learning program content to determine the best matching course to
replace an existing instructor-led training (ILT) course, as well
as identify additional content that needs to be sourced and/or
created to achieve coverage of a desired training course. Such a
process is generally referred to (and is referred to herein) as
mapping.
[0003] In such conventional approaches as noted above, the mapping
process is both labor- and time-intensive. Moreover, the outcome of
a conventional mapping process generally depends significantly on
the experience and/or expertise of the instructional designer
performing the mapping.
SUMMARY
[0004] In one embodiment of the present invention, techniques for
cognitive content mapping and collating are provided. An exemplary
computer-implemented method can include identifying one or more
resources relevant to an existing course, and partitioning, based
on one or more pre-determined partitioning parameters, (i) the
existing course into multiple portions and (ii) the one or more
resources into multiple portions. Such a method can also include
detecting one or more content coverage gaps in the existing course
by semantically comparing (i) the multiple portions of the existing
course with (ii) the multiple portions of the one or more
resources, and retrieving, based on the one or more detected
content coverage gaps, at least one of the multiple portions of the
one or more resources. One or more embodiments can also include
using structured meta-data as part of this comparison process.
Further, such an exemplary method as described herein can include
generating an updated version of the existing course by
incorporating the at least one retrieved portion of the one or more
resources into the existing course.
[0005] In another embodiment of the invention, an exemplary
computer-implemented method can include identifying one or more
external resources relevant to an existing training course
associated with a given enterprise, and partitioning, based on one
or more pre-determined partitioning parameters, (i) the existing
training course into multiple consumable modules and (ii) the one
or more external resources into multiple consumable modules. Such a
method can also include detecting one or more content coverage gaps
in the existing training course by semantically comparing (i) the
multiple consumable modules of the existing training course with
(ii) the multiple consumable modules of the one or more external
resources, and automatically creating one or more work items,
wherein the one or more work items provide (i) instructions for
generating an updated version of the existing training course based
on incorporating at least one of the multiple consumable modules of
the one or more external resources into the existing training
course and (ii) an estimated effort associated with generating the
updated version of the existing training course. Further, such a
method can also include outputting the one or more created work
items to one or more instructional designers associated with the
given enterprise.
[0006] Another embodiment of the invention or elements thereof can
be implemented in the form of a computer program product tangibly
embodying computer readable instructions which, when implemented,
cause a computer to carry out a plurality of method steps, as
described herein. Furthermore, another embodiment of the invention
or elements thereof can be implemented in the form of a system
including a memory and at least one processor that is coupled to
the memory and configured to perform noted method steps. Yet
further, another embodiment of the invention or elements thereof
can be implemented in the form of means for carrying out the method
steps described herein, or elements thereof; the means can include
hardware module(s) or a combination of hardware and software
modules, wherein the software modules are stored in a tangible
computer-readable storage medium (or multiple such media).
[0007] These and other objects, features and advantages of the
present invention will become apparent from the following detailed
description of illustrative embodiments thereof, which is to be
read in connection with the accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] FIG. 1 is a diagram illustrating system architecture,
according to an exemplary embodiment of the invention;
[0009] FIG. 2 is a diagram illustrating document representation,
according to an exemplary embodiment of the invention;
[0010] FIG. 3 is a diagram illustrating course similarity
determinations, according to an exemplary embodiment of the
invention;
[0011] FIG. 4 is a diagram illustrating a content collator,
according to an exemplary embodiment of the invention;
[0012] FIG. 5 is a flow diagram illustrating techniques according
to an embodiment of the invention;
[0013] FIG. 6 is a system diagram of an exemplary computer system
on which at least one embodiment of the invention can be
implemented;
[0014] FIG. 7 depicts a cloud computing environment according to an
embodiment of the present invention; and
[0015] FIG. 8 depicts abstraction model layers according to an
embodiment of the present invention.
DETAILED DESCRIPTION
[0016] As described herein, an embodiment of the present invention
includes cognitive content mapping and collating. At least one
embodiment of the invention includes generating and implementing a
cognitive system that suggests electronic learning (e-learning)
courses and/or additional content in connection with one or more
ILT courses and/or other existing courseware. Such an embodiment
can include automatically analyzing ILT and e-learning course
content and generating design guidance to effectively and
efficiently map courseware.
[0017] Additionally, one or more embodiments of the invention
include providing information to expedite content creation for
instruction designers (IDs). Further, such an embodiment includes
enabling IDs to identify and/or understand gaps in existing course
content, as well as enabling the IDs to use existing resources to
augment their existing courses and/or create new content. Such
resources can include, for example, content such as blogs,
articles, online videos, etc.
[0018] At least one embodiment of the invention includes
determining whether an e-learning course provides sufficient
coverage of particular topics and/or content in connection with an
ILT course. Such an embodiment can include identifying other
material that covers topics and/or content in an ILT course, and
enabling automated creation of a new course covering a set of one
or more learning objectives (or based on results from a
user-specified search), using modules of an existing ILT or
e-learning course. In using user-specified search results to create
a new course, a user can, for example, select documents (chunks)
that are of interest and request the generation of a new course
based thereon. Such a request can include, for instance, a zip
export, a generation into sharable content object reference model
(SCORM) format, one or more learning formats that take multiple
learning objects (files) and order them based on some criteria
(which could also be further user input) or course curriculum
guidelines, etc. Also, one or more embodiments of the invention can
include implementing a framework for a user-driven creation of a
course based on system-generated recommendations.
[0019] As also detailed herein, at least one embodiment of the
invention includes automatically updating existing course content
with available e-learning course content by mapping similarities
between the existing course content and the available course
content. Such an embodiment can additionally include identifying
one or more gaps between ILT course content and available
e-learning course content by mapping portions of e-learning course
material with the ILT course content, and automatically creating
work items to bridge the identified gaps by adding new and/or
alternate content to the ILT course. Further, such an embodiment
can include exporting the updated ILT course content, for example,
into a standard courseware format, a SCORM format, a custom format,
as a collection of documents in a zip file, etc.
[0020] One or more embodiments of the invention can include
semantically portioning ILT courses into consumable modules based
on learning objectives. Consumable modules can refer to learning
objects that are smaller than large courses. For example,
consumable objects can focus on a few (1-5, for instance) learning
objectives instead of many dozens of learning objectives that large
courses can often encompass. Such an embodiment can also include
semantically comparing the ILT course modules with modules from
e-learning courses to detect matches and coverage gaps in the ILT
courses (leveraging multi-media analytics techniques). Via one or
more multi-media analytics techniques, for example, images can be
captioned, optical character recognition (OCR) can be performed so
that more is known about the content presented, and such
information could then be used in the overall matching, etc. Based
on the identified gaps, such an embodiment can include retrieving
alternate content that address the gaps (or parts thereof) and
demonstrates overall affinity with the ILT course.
[0021] Additionally, work items can be automatically created for
instructional design team members to bridge gaps using new and/or
alternate content, and the effort required for making those changes
can be estimated. By way of example, an effort estimation can be
implemented as a regression model that uses features from previous
course material and the course creation time (historical). Example
features can include course topics, lexical and semantic features,
topical analysis via external sources, learning objectives to be
covered, presentation style, etc. One or more embodiments of the
invention can also include ranking e-learning courses based on
levels of matching and estimated mapping effort for an existing ILT
course. Further, such an embodiment can include assisting an ID
team to execute a set of mapping tasks, automatically generate the
correct sequence of e-learning and additional modules, and updating
the effort estimate models. The correct sequence can be based, for
example, on course and/or learning goals/objectives. Course
objectives, for example, are typically presented with a lesson
plan.
[0022] One or more embodiments of the invention can include
operating under one or more assumptions. Such assumptions can
include, for example, that an ILT course includes a sequence of
files in one or more formats such as .doc, .ppt, .pdf, etc. Such
assumptions can also include that an e-learning course includes
content in SCORM packages with individual files in formats of .doc,
ppt, .pdf, etc., and also videos, simulations, animation, and/or
other file types. Further, such assumptions can include that
equivalent text (for example, a transcript) is available for all
files in non-text format in an e-learning course (or can be
provided by a pluggable module). It can also be assumed in one or
more embodiments of the invention that ILT and e-learning parent
courses have been chunked or partitioned into modules based on
topic and length. For example, a primary topic in a parent course
may be distributed over multiple modules after chunking, with
linkages maintained between the modules. Additionally, each module
can include parameters such as, for example, a maximum temporal
parameter (for example, a maximum of N minutes in length (which can
be determined automatically using heuristics, by aggregating
average reading stats, etc.). Such assumptions can also include
that partitioned chunks have been labeled and/or tagged with one or
more learning objectives.
[0023] FIG. 1 is a diagram illustrating system architecture,
according to an embodiment of the invention. By way of
illustration, FIG. 1 depicts a course modularizer 104, which, based
on one or more input learning objectives, modularizes an ILT course
derived from an ILT course database 102. The modularized ILT
course, in addition to one or more input e-learning courses, are
provided to a search and comparison component 106. The search and
comparison component 106 includes an alternate search and affinity
measurement module 117, and a semantic module comparator 119 (which
carries out matching functions, coverage discovery functions, and
gap determination functions).
[0024] With respect to the semantic module comparator 119, a user
can select an ILT course and query the semantic module comparator
119 to map the ILT course to one or more e-learning courses. The
semantic module comparator 119 generates and returns a ranked list
of candidate e-learning courses, with summary information on each
match. The user can select a search result and navigate (for
example, scroll through) a map that shows how the modules (chunks)
of the ILT course map to modules of the e-learning course. For each
module-level mapping, the semantic module comparator 119 can
provide additional insights on gaps and possible actions, and
assist the user in creating work-items to complete the mapping.
[0025] Additionally, at least one embodiment of the invention can
include mapping, via the alternate content search and affinity
measurement module 117, for example, learning objectives to
e-learning courses. The user selects a learning objective (or a set
of learning objectives) and queries module 117 to map the learning
objective(s) to one or more e-learning courses. Module 117
generates and returns a ranked list of candidate e-learning
courses, with summary information on each match. The user can
select a search result and scroll through a map that shows how the
modules (chunks) of the courses map to different learning
objectives. For each mapping, module 117 can provide additional
insights on gaps and possible actions, and assist the user in
creating work-items to complete the mapping.
[0026] As illustrated, FIG. 1 also depicts an ILT mapper, explorer
and selector component 108 (which can be associated with an ID team
or lead operator), which receives inputs (such as match scores,
module gaps, and content) from component 106. The ILT mapper,
explorer and selector component generates an ILT-e-learning
course(s) pairing as an output, which is then provided to an ID
work item creation component 110. The pairing can be based on
similarity, using the scores and the meta-data generated by module
117. The ID work item creation component 110 provides a work item
to ID work item management component 112, which can be associated
with an ID specialist. The ID work item management component 112
processes the work item and generates an output to a course
collator 114. For example, for gaps identified, the ID work item
management component 112 can route course creation requests to
instruction designers based on their domain expertise, time
availability (versus mapping effort computed), etc. The course
collator 114 can view identified existing courses and/or related
courses, gaps, etc., which a designer can then use to seed or
create new content. Additionally, the ID work item management
component 112 outputs a mapping effort to mapping effort estimator
116. The mapping effort generated by component 112 allows a user to
identify a new gap and/or an improvement which he or she does not
want to and/or cannot work on, and the mapping effort can then be
used to map a new effort and be put into the queue as a new work
item.
[0027] The mapping effort estimator 116 estimates the time required
to generate new course content considering the gaps in the mapping.
In at least one embodiment of the invention, the mapping effort
estimator 116 can be implemented as a regression model that uses
features from previous course material and the course creation
time. Example features can include course topics, lexical and
semantic features, topical analysis via external sources, learning
objectives to be covered, presentation style, etc. The mapping
effort estimator 116 instantiates a work flow for the ID to create
new content, and the new content creation can be tracked and fed
back to the system (via component 116) for improving mapping effort
and/or content creation estimation.
[0028] FIG. 2 is a diagram illustrating document representation,
according to an exemplary embodiment of the invention. By way of
illustration, FIG. 2 depicts an ILT course, an e-learning course,
and/or other resource 202, which has been modularized into multiple
distinct modules. Based on the distinct modules, the ILT course,
e-learning course, and/or other resource is partitioned into
multiple chunks 204, wherein, for example, chunk i.j is the
j.sup.th chunk of the i.sup.th module. Additionally, based on the
partitioned chunks, a vector representation 206 of the ILT course,
e-learning course, and/or other resource is created. Accordingly,
in one or more embodiments of the invention, all documents and
their chunks are represented as vectors. The vectors can be
constructed using methods such as, for example, term
frequency-inverse document frequency (tf-idf), and/or generating
vectors via a neural network can provide a non-tf-idf based
vector.
[0029] FIG. 3 is a diagram illustrating course similarity
determinations, according to an exemplary embodiment of the
invention. By way of illustration, FIG. 3 depicts vectors of a
first course 302 mapped to vectors of a second course 304. As noted
above and herein, one or more embodiments of the invention include
creating and utilizing vectors which correspond to chunks of
courses or other external resources. In such an embodiment, all
chunks retain logical meta-data linking them to their parent
resources (documents, course and module). Document-level similarity
can include, for example, cosine similarity, and scores can be
binned for high, medium, and low classification.
[0030] At least one embodiment of the invention can include
utilizing the edges between chunk vectors, wherein each edge
denotes the cosine similarity score between the two vectors of the
chunks. Such an embodiment can also include computing the maximum
match in the bi-partite graph, and using weighted average of
maximal edges, generating a combined similarity score (using a
maximum match algorithm) between the two courses. As used herein, a
maximal edge refers to an edge that participates in the maximum
matching (based on a maximum matching algorithm).
[0031] Also, to enforce scores taking into account the relative
ordering of courses, one or more embodiments of the invention can
include using the following constraint for the maximum match: if
a_i-b_j (i>j) is selected as a maximal edge, then there can be
no edge selected between a_(i+x) and b_(j-y), x, y>0 and
(i+x)<m. As used above and herein, a_i represents any node on
the left half of the bi-partite graph, b_j represents any node on
the right half of the bi-partite graph, i and j represent indices,
and x and y represent integers.
[0032] FIG. 4 is a diagram illustrating a content collator,
according to an exemplary embodiment of the invention. By way of
illustration, FIG. 4 depicts a content collator 402, which, based
on inputs of one or more selected documents (such as a first
chunk/document 416, a second chunk/document 418, an n.sup.th
chunk/document 420), generates a custom course 422. As also
illustrated in FIG. 4, the content collator 402 includes a format
and/or presentation standardizer 404, a chunk summarizer and/or
introduction generation component 406, an accessibility enabler 408
(for caption generation, etc.), a chunk interlinking text generator
410, an export utility component 412, and one or more other
pluggable modules 414 (such as a user interface, for example).
[0033] The format and/or presentation standardizer 404 reformats
documents for rendering and/or presentation consistency based on
existing documents and/or input. The chunk summarizer and/or
introduction generation component 406 and the chunk interlinking
text generator 410 generate bridging text based on existing text.
For example, one or more embodiments of the invention can include
templatized text generation based on topics or neural methods for
text generation that can be based on sequence-to-sequence
architectures. The accessibility enabler 408 adds captions to
images, videos, etc. The accessibility enabler 408 can implement
one or more algorithms (such as, for example, OCR, image/video
captioning based on speech and scene features, text-to-speech audio
generation, etc.) to carry out its function(s). Additionally, the
export utility component 412 generates a course in a zip, SCORM, or
other required format (which can be customizable by the user).
[0034] In at least one embodiment of the invention, all documents
(such as chunks/documents 416, 418 and 420) are tagged with one or
more learning objectives and additional metadata. In such an
embodiment, a user can select one or more documents (or chunks)
that are of interest and can further request the generation of a
new course 422 (based on the selected documents (chunks)).
Recommendations for chunks/documents to be selected can also be
considered using suggestions from a cognitive content laboratory
(based on target audience, etc.). The content collator 402 reads
the text of the selected chunks/documents, and standardizes the
text based on a new format template (for example, via standardizer
404). Via accessibility enabler 408, any multi-media content in the
selected chunks/documents can be automatically captioned, text
colors can be enhanced, etc., for improving accessibility. The
different chunks/documents can be linked together via the chunk
interlinking text generator 410 by auto-generating bridge text.
Additionally, the collated chunks/documents can be exported via
export utility component 412 in standard course formats for
use.
[0035] FIG. 5 is a flow diagram illustrating techniques according
to an embodiment of the present invention. Step 502 includes
identifying one or more resources relevant to an existing course.
The existing course can include, for example, an ILT course, and
the one or more resources can include, for example, one or more
e-learning courses.
[0036] Step 504 includes partitioning, based on one or more
pre-determined partitioning parameters, (i) the existing course
into multiple portions and (ii) the one or more resources into
multiple portions. Partitioning can include semantically
partitioning (i) the existing course into multiple consumable
portions and (ii) the one or more resources into multiple
consumable portions. Additionally, the one or more pre-determined
partitioning parameters can include one or more learning
objectives, one or more keywords, etc.
[0037] Step 506 includes detecting one or more content coverage
gaps in the existing course by semantically comparing (i) the
multiple portions of the existing course with (ii) the multiple
portions of the one or more resources. Detecting the one or more
content coverage gaps in the existing course can include
implementing one or more multimedia analytics techniques.
Additionally, detecting the one or more content coverage gaps in
the existing course can include mapping (i) the multiple portions
of the existing course to (ii) the multiple portions of the one or
more resources. Further, semantically comparing can include
comparing (i) metadata associated with the multiple portions of the
existing course with (ii) metadata associated with the multiple
portions of the one or more resources.
[0038] Step 508 includes retrieving, based on the one or more
detected content coverage gaps, at least one of the multiple
portions of the one or more resources. At least one embodiment of
the invention can also include determining affinity of the at least
one retrieved portion of the one or more resources with the
existing course.
[0039] Step 510 includes generating an updated version of the
existing course by incorporating the at least one retrieved portion
of the one or more resources into the existing course.
[0040] The techniques depicted in FIG. 5 can also include
standardizing formatting of (i) the existing course and (ii) the
one or more resources. Further, one or more embodiments of the
invention can include generating, within the updated version of the
existing course, one or more captions in connection with one or
more items of multimedia content, as well as generating, within the
updated version of the existing course, one or more items of bridge
text linking two or more of multiple portions of the updated
version of the existing course. Also, the techniques depicted in
FIG. 5 can additionally include exporting the updated version of
the existing course to one or more formats, wherein the one or more
formats can include a standard courseware format, a sharable
content object reference model, a custom format, and/or a
collection of documents in a zip file.
[0041] Also, an additional embodiment of the invention includes
identifying one or more external resources relevant to an existing
training course associated with a given enterprise, and
partitioning, based on one or more pre-determined partitioning
parameters, (i) the existing training course into multiple
consumable modules and (ii) the one or more external resources into
multiple consumable modules. Such an embodiment can also include
detecting one or more content coverage gaps in the existing
training course by semantically comparing (i) the multiple
consumable modules of the existing training course with (ii) the
multiple consumable modules of the one or more external resources,
and automatically creating one or more work items, wherein the one
or more work items provide (i) instructions for generating an
updated version of the existing training course based on
incorporating at least one of the multiple consumable modules of
the one or more external resources into the existing training
course and (ii) an estimated effort associated with generating the
updated version of the existing training course. Further, such an
embodiment can also include outputting the one or more created work
items to one or more instructional designers associated with the
given enterprise.
[0042] The techniques depicted in FIG. 5 can also, as described
herein, include providing a system, wherein the system includes
distinct software modules, each of the distinct software modules
being embodied on a tangible computer-readable recordable storage
medium. All of the modules (or any subset thereof) can be on the
same medium, or each can be on a different medium, for example. The
modules can include any or all of the components shown in the
figures and/or described herein. In an embodiment of the invention,
the modules can run, for example, on a hardware processor. The
method steps can then be carried out using the distinct software
modules of the system, as described above, executing on a hardware
processor. Further, a computer program product can include a
tangible computer-readable recordable storage medium with code
adapted to be executed to carry out at least one method step
described herein, including the provision of the system with the
distinct software modules.
[0043] Additionally, the techniques depicted in FIG. 5 can be
implemented via a computer program product that can include
computer useable program code that is stored in a computer readable
storage medium in a data processing system, and wherein the
computer useable program code was downloaded over a network from a
remote data processing system. Also, in an embodiment of the
invention, the computer program product can include computer
useable program code that is stored in a computer readable storage
medium in a server data processing system, and wherein the computer
useable program code is downloaded over a network to a remote data
processing system for use in a computer readable storage medium
with the remote system.
[0044] An embodiment of the invention or elements thereof can be
implemented in the form of an apparatus including a memory and at
least one processor that is coupled to the memory and configured to
perform exemplary method steps.
[0045] Additionally, an embodiment of the present invention can
make use of software running on a computer or workstation. With
reference to FIG. 6, such an implementation might employ, for
example, a processor 602, a memory 604, and an input/output
interface formed, for example, by a display 606 and a keyboard 608.
The term "processor" as used herein is intended to include any
processing device, such as, for example, one that includes a CPU
(central processing unit) and/or other forms of processing
circuitry. Further, the term "processor" may refer to more than one
individual processor. The term "memory" is intended to include
memory associated with a processor or CPU, such as, for example,
RAM (random access memory), ROM (read only memory), a fixed memory
device (for example, hard drive), a removable memory device (for
example, diskette), a flash memory and the like. In addition, the
phrase "input/output interface" as used herein, is intended to
include, for example, a mechanism for inputting data to the
processing unit (for example, mouse), and a mechanism for providing
results associated with the processing unit (for example, printer).
The processor 602, memory 604, and input/output interface such as
display 606 and keyboard 608 can be interconnected, for example,
via bus 610 as part of a data processing unit 612. Suitable
interconnections, for example via bus 610, can also be provided to
a network interface 614, such as a network card, which can be
provided to interface with a computer network, and to a media
interface 616, such as a diskette or CD-ROM drive, which can be
provided to interface with media 618.
[0046] Accordingly, computer software including instructions or
code for performing the methodologies of the invention, as
described herein, may be stored in associated memory devices (for
example, ROM, fixed or removable memory) and, when ready to be
utilized, loaded in part or in whole (for example, into RAM) and
implemented by a CPU. Such software could include, but is not
limited to, firmware, resident software, microcode, and the
like.
[0047] A data processing system suitable for storing and/or
executing program code will include at least one processor 602
coupled directly or indirectly to memory elements 604 through a
system bus 610. The memory elements can include local memory
employed during actual implementation of the program code, bulk
storage, and cache memories which provide temporary storage of at
least some program code in order to reduce the number of times code
must be retrieved from bulk storage during implementation.
[0048] Input/output or I/0 devices (including, but not limited to,
keyboards 608, displays 606, pointing devices, and the like) can be
coupled to the system either directly (such as via bus 610) or
through intervening I/O controllers (omitted for clarity).
[0049] Network adapters such as network interface 614 may also be
coupled to the system to enable the data processing system to
become coupled to other data processing systems or remote printers
or storage devices through intervening private or public networks.
Modems, cable modems and Ethernet cards are just a few of the
currently available types of network adapters.
[0050] As used herein, including the claims, a "server" includes a
physical data processing system (for example, system 612 as shown
in FIG. 6) running a server program. It will be understood that
such a physical server may or may not include a display and
keyboard.
[0051] The present invention may be a system, a method, and/or a
computer program product at any possible technical detail level of
integration. The computer program product may include a computer
readable storage medium (or media) having computer readable program
instructions thereon for causing a processor to carry out
embodiments of the present invention.
[0052] The computer readable storage medium can be a tangible
device that can retain and store instructions for use by an
instruction execution device. The computer readable storage medium
may be, for example, but is not limited to, an electronic storage
device, a magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
[0053] Computer readable program instructions described herein can
be downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
[0054] Computer readable program instructions for carrying out
operations of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, configuration data for integrated
circuitry, or either source code or object code written in any
combination of one or more programming languages, including an
object oriented programming language such as Smalltalk, C++, or the
like, and procedural programming languages, such as the "C"
programming language or similar programming languages. The computer
readable program instructions may execute entirely on the user's
computer, partly on the user's computer, as a stand-alone software
package, partly on the user's computer and partly on a remote
computer or entirely on the remote computer or server. In the
latter scenario, the remote computer may be connected to the user's
computer through any type of network, including a local area
network (LAN) or a wide area network (WAN), or the connection may
be made to an external computer (for example, through the Internet
using an Internet Service Provider). In some embodiments,
electronic circuitry including, for example, programmable logic
circuitry, field-programmable gate arrays (FPGA), or programmable
logic arrays (PLA) may execute the computer readable program
instructions by utilizing state information of the computer
readable program instructions to personalize the electronic
circuitry, in order to perform embodiments of the present
invention.
[0055] Embodiments of the present invention are described herein
with reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
[0056] These computer readable program instructions may be provided
to a processor of a general purpose computer, special purpose
computer, or other programmable data processing apparatus to
produce a machine, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowchart and/or block
diagram block or blocks.
[0057] The computer readable program instructions may also be
loaded onto a computer, other programmable data processing
apparatus, or other device to cause a series of operational steps
to be performed on the computer, other programmable apparatus or
other device to produce a computer implemented process, such that
the instructions which execute on the computer, other programmable
apparatus, or other device implement the functions/acts specified
in the flowchart and/or block diagram block or blocks.
[0058] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the blocks may occur out of the order noted in
the Figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
[0059] It should be noted that any of the methods described herein
can include an additional step of providing a system comprising
distinct software modules embodied on a computer readable storage
medium; the modules can include, for example, any or all of the
components detailed herein. The method steps can then be carried
out using the distinct software modules and/or sub-modules of the
system, as described above, executing on a hardware processor 602.
Further, a computer program product can include a computer-readable
storage medium with code adapted to be implemented to carry out at
least one method step described herein, including the provision of
the system with the distinct software modules.
[0060] In any case, it should be understood that the components
illustrated herein may be implemented in various forms of hardware,
software, or combinations thereof, for example, application
specific integrated circuit(s) (ASICS), functional circuitry, an
appropriately programmed digital computer with associated memory,
and the like. Given the teachings of the invention provided herein,
one of ordinary skill in the related art will be able to
contemplate other implementations of the components of the
invention.
[0061] Additionally, it is understood in advance that
implementation of the teachings recited herein are not limited to a
particular computing environment. Rather, embodiments of the
present invention are capable of being implemented in conjunction
with any type of computing environment now known or later
developed.
[0062] For example, cloud computing is a model of service delivery
for enabling convenient, on-demand network access to a shared pool
of configurable computing resources (for example, networks, network
bandwidth, servers, processing, memory, storage, applications,
virtual machines, and services) that can be rapidly provisioned and
released with minimal management effort or interaction with a
provider of the service. This cloud model may include at least five
characteristics, at least three service models, and at least four
deployment models.
[0063] Characteristics are as follows:
[0064] On-demand self-service: a cloud consumer can unilaterally
provision computing capabilities, such as server time and network
storage, as needed automatically without requiring human
interaction with the service's provider.
[0065] Broad network access: capabilities are available over a
network and accessed through standard mechanisms that promote use
by heterogeneous thin or thick client platforms (e.g., mobile
phones, laptops, and PDAs).
[0066] Resource pooling: the provider's computing resources are
pooled to serve multiple consumers using a multi-tenant model, with
different physical and virtual resources dynamically assigned and
reassigned according to demand. There is a sense of location
independence in that the consumer generally has no control or
knowledge over the exact location of the provided resources but may
be able to specify location at a higher level of abstraction (for
example, country, state, or datacenter).
[0067] Rapid elasticity: capabilities can be rapidly and
elastically provisioned, in some cases automatically, to quickly
scale out and rapidly released to quickly scale in. To the
consumer, the capabilities available for provisioning often appear
to be unlimited and can be purchased in any quantity at any
time.
[0068] Measured service: cloud systems automatically control and
optimize resource use by leveraging a metering capability at some
level of abstraction appropriate to the type of service (for
example, storage, processing, bandwidth, and active user accounts).
Resource usage can be monitored, controlled, and reported providing
transparency for both the provider and consumer of the utilized
service.
[0069] Service Models are as follows:
[0070] Software as a Service (SaaS): the capability provided to the
consumer is to use the provider's applications running on a cloud
infrastructure. The applications are accessible from various client
devices through a thin client interface such as a web browser (for
example, web-based e-mail). The consumer does not manage or control
the underlying cloud infrastructure including network, servers,
operating systems, storage, or even individual application
capabilities, with the possible exception of limited user-specific
application configuration settings.
[0071] Platform as a Service (PaaS): the capability provided to the
consumer is to deploy onto the cloud infrastructure
consumer-created or acquired applications created using programming
languages and tools supported by the provider. The consumer does
not manage or control the underlying cloud infrastructure including
networks, servers, operating systems, or storage, but has control
over the deployed applications and possibly application hosting
environment configurations.
[0072] Infrastructure as a Service (IaaS): the capability provided
to the consumer is to provision processing, storage, networks, and
other fundamental computing resources where the consumer is able to
deploy and run arbitrary software, which can include operating
systems and applications. The consumer does not manage or control
the underlying cloud infrastructure but has control over operating
systems, storage, deployed applications, and possibly limited
control of select networking components (for example, host
firewalls).
[0073] Deployment Models are as follows:
[0074] Private cloud: the cloud infrastructure is operated solely
for an organization. It may be managed by the organization or a
third party and may exist on-premises or off-premises.
[0075] Community cloud: the cloud infrastructure is shared by
several organizations and supports a specific community that has
shared concerns (for example, mission, security requirements,
policy, and compliance considerations). It may be managed by the
organizations or a third party and may exist on-premises or
off-premises.
[0076] Public cloud: the cloud infrastructure is made available to
the general public or a large industry group and is owned by an
organization selling cloud services.
[0077] Hybrid cloud: the cloud infrastructure is a composition of
two or more clouds (private, community, or public) that remain
unique entities but are bound together by standardized or
proprietary technology that enables data and application
portability (for example, cloud bursting for load-balancing between
clouds).
[0078] A cloud computing environment is service oriented with a
focus on statelessness, low coupling, modularity, and semantic
interoperability. At the heart of cloud computing is an
infrastructure comprising a network of interconnected nodes.
[0079] Referring now to FIG. 7, illustrative cloud computing
environment 50 is depicted. As shown, cloud computing environment
50 includes one or more cloud computing nodes 10 with which local
computing devices used by cloud consumers, such as, for example,
personal digital assistant (PDA) or cellular telephone 54A, desktop
computer 54B, laptop computer 54C, and/or automobile computer
system 54N may communicate. Nodes 10 may communicate with one
another. They may be grouped (not shown) physically or virtually,
in one or more networks, such as Private, Community, Public, or
Hybrid clouds as described hereinabove, or a combination thereof.
This allows cloud computing environment 50 to offer infrastructure,
platforms and/or software as services for which a cloud consumer
does not need to maintain resources on a local computing device. It
is understood that the types of computing devices 54A-N shown in
FIG. 7 are intended to be illustrative only and that computing
nodes 10 and cloud computing environment 50 can communicate with
any type of computerized device over any type of network and/or
network addressable connection (e.g., using a web browser).
[0080] Referring now to FIG. 8, a set of functional abstraction
layers provided by cloud computing environment 50 (FIG. 7) is
shown. It should be understood in advance that the components,
layers, and functions shown in FIG. 8 are intended to be
illustrative only and embodiments of the invention are not limited
thereto. As depicted, the following layers and corresponding
functions are provided:
[0081] Hardware and software layer 60 includes hardware and
software components. Examples of hardware components include:
mainframes 61; RISC (Reduced Instruction Set Computer) architecture
based servers 62; servers 63; blade servers 64; storage devices 65;
and networks and networking components 66. In some embodiments,
software components include network application server software 67
and database software 68.
[0082] Virtualization layer 70 provides an abstraction layer from
which the following examples of virtual entities may be provided:
virtual servers 71; virtual storage 72; virtual networks 73,
including virtual private networks; virtual applications and
operating systems 74; and virtual clients 75. In one example,
management layer 80 may provide the functions described below.
Resource provisioning 81 provides dynamic procurement of computing
resources and other resources that are utilized to perform tasks
within the cloud computing environment. Metering and Pricing 82
provide cost tracking as resources are utilized within the cloud
computing environment, and billing or invoicing for consumption of
these resources.
[0083] In one example, these resources may include application
software licenses. Security provides identity verification for
cloud consumers and tasks, as well as protection for data and other
resources. User portal 83 provides access to the cloud computing
environment for consumers and system administrators. Service level
management 84 provides cloud computing resource allocation and
management such that required service levels are met. Service Level
Agreement (SLA) planning and fulfillment 85 provide pre-arrangement
for, and procurement of, cloud computing resources for which a
future requirement is anticipated in accordance with an SLA.
[0084] Workloads layer 90 provides examples of functionality for
which the cloud computing environment may be utilized. Examples of
workloads and functions which may be provided from this layer
include: mapping and navigation 91; software development and
lifecycle management 92; virtual classroom education delivery 93;
data analytics processing 94; transaction processing 95; and
cognitive content mapping and collating 96, in accordance with the
one or more embodiments of the present invention.
[0085] The terminology used herein is for the purpose of describing
particular embodiments only and is not intended to be limiting of
the invention. As used herein, the singular forms "a," "an" and
"the" are intended to include the plural forms as well, unless the
context clearly indicates otherwise. It will be further understood
that the terms "comprises" and/or "comprising," when used in this
specification, specify the presence of stated features, steps,
operations, elements, and/or components, but do not preclude the
presence or addition of another feature, step, operation, element,
component, and/or group thereof.
[0086] At least one embodiment of the present invention may provide
a beneficial effect such as, for example, identifying gaps between
ILT course content and e-learning course content, and automatically
creating work-items to bridge the gaps by adding new and/or
alternate content.
[0087] The descriptions of the various embodiments of the present
invention have been presented for purposes of illustration, but are
not intended to be exhaustive or limited to the embodiments
disclosed. Many modifications and variations will be apparent to
those of ordinary skill in the art without departing from the scope
and spirit of the described embodiments. The terminology used
herein was chosen to best explain the principles of the
embodiments, the practical application or technical improvement
over technologies found in the marketplace, or to enable others of
ordinary skill in the art to understand the embodiments disclosed
herein.
* * * * *